• YAKHAK HOEJI
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• v.49 no.2
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• pp.168-173
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• 2005

Alzheimers disease (AD) is the most prevalent form of neurodegenerations associated with aging in the human population. This disease is characterized by the extracellular deposition of beta-amyloid (A ${\beta}$) peptide in cerebral plaques. The A ${\beta}$ peptide is derived from the ${\beta}$-amyloid precursor protein ( ${\beta}$APP). Photolytic processing of ${\beta}$APP by ${\beta}$-secretase(beta-site APP-cleaving enzyme, BASE) and ${\gamma}$-secretase generates the A ${\beta}$ peptide. Several lines of evidence support that A ${\beta}$-induced neuronal cell death is major mechanisms of development of AD. Accordingly, the ${\beta}$-and ${\gamma}$-secretase have been implicated to be excellent targets for the treatment of AD. We previously found that sesaminol glucosides have improving effect on memory functions through anti-oxidative mechanism. In this study, to elucidate possible other mechanism (inhibition of ${\beta}$-and ${\gamma}$-secretase) of sesaminol glucosides, we examined the improving effect of sesaminol glucosides in the scopolamine (1 mg/kg/mouse)-induced memory dysfunction using water maze test in the mice. Sesaminol glucosides (3.75, 7.5 mg/kg/6ml/day p.o., for 3 weeks) reversed the latency time, distance and velocity by scopolamine in dose dependent manner. Next, ${\beta}$-and ${\gamma}$-secretase activities were determined in different regions of brain. Sesaminol glucosides dose-dependently attenuated scopolamine-induced ${\beta}$-secretase activities in cortex and hippocampous and ${\gamma}$-secretase in cortex. This study therefore suggests that sesaminol glucosides may be a useful agent for prevention of the development or progression of AD, and its inhibitory effect on secretase may play a role in the improving action of sesaminol glucosides on memory function.

• The KIPS Transactions:PartA
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• v.8A no.3
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• pp.189-200
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• 2001

This paper proposes a memory allocation model for Real-Time Linux. The proposed model allows users to create several continuous memory regions in an application, to specify an appropriate region allocation policy for each memory region, and to request memory blocks from a necessary memory region. Instead of using single memory management module in order to support the proposed model, we adopt two-layered structure that is consisted of region allocators implementing allocation policies and a region manager controlling regions and region allocator modules. This structure separates allocation policy from allocation mechanism, thus allows system developers to implement same allocation policy using different algorithms in case of need. IN addition, it enables them to implement new allocation policy using different algorithms in case of need. In addition, it enables them to implement new allocation policy easily as long as they preserver predefined internal interfaces, to add the implemented policy into the system, and to remove unnecessary allocation policies from the system, Because the proposed model provides various allocation policies implemented previously, system builders can also reconfigure the system by just selecting most appropriate policies for a specific application without implementing these policies from scratch.

• Journal of KIISE:Computing Practices and Letters
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• v.15 no.11
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• pp.836-840
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• 2009

Many researchers have studied flash memory in order to replace hard disk storages. Many FTL algorithms have been proposed to overcome physical constraints of flash memory such as erase-before-write, wear leveling, and poor write performance. Therefore, these constraints should be considered for testing FTL algorithms and the performance evaluation of flash memory. As doing the experiments, we suffer from several problems with costs and settings in experimental configuration. When we, for example, replay the traces of Oracle to evaluate the I/O performance with flash memory, it is hard to extract exact traces of I/O operations in Oracle. Since there are only write operations in the log, it is impossible to gather read operations. In MySQL and SQLite, we can gather the read operations by changing I/O functions in the source codes. But it is not easy to search for the exact points about I/O and even if we can find out the points, we might get wrong results depending on how we modify source codes to get I/O traces. The FlaSim proposed in this paper removes the difficulties when we evaluate the performance of FTL algorithms and flash memory. Our Linux drivers emulate the flash memory as a hard disk. And we can easily obtain the usage statistics of flash memory such as the number of write, read, and erase operations. The FlaSim can be gracefully extended to support the additional modules implemented by novel algorithms and ideas. In this paper, we describe the structure of FTL emulator, development tools and operating methods. We expect this emulator to be helpful for many experiments and research with flash memory.

• The Journal of the Acoustical Society of Korea
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• v.30 no.8
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• pp.461-467
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• 2011

Accurate classification of input signals is the key prerequisite for variable bit-rate coding, which has been introduced in order to effectively utilize limited communication bandwidth. Especially, recent surge of multimedia services elevate the importance of speech/music classification. Among many speech/music classifier, the ones based on support vector machine (SVM) have a strong selling point, high classification accuracy, but their computational complexity and memory requirement hinder their way into actual implementations. Therefore, techniques that reduce the computational complexity and the memory requirement is inevitable, particularly for embedded systems. We first analyze implementation of an SVM-based classifier on embedded systems in terms of execution time and energy consumption, and then propose two techniques that alleviate the implementation requirements: One is a technique that removes support vectors that have insignificant contribution to the final classification, and the other is to skip processing some of input signals by virtue of strong correlations in speech/music frames. These are post-processing techniques that can work with any other optimization techniques applied during the training phase of SVM. With experiments, we validate the proposed algorithms from the perspectives of classification accuracy, execution time, and energy consumption.

• The Journal of Society for e-Business Studies
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• v.23 no.2
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• pp.33-47
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• 2018

City gas pipelines are buried underground. Because of this, pipeline is hard to manage, and can be easily damaged. This research proposes a real time prediction system that helps experts can make decision about pressure anomalies. The gas pipline pressure data of Jungbu City Gas Company, which is one of the domestic city gas suppliers, time variables and environment variables are analysed. In this research, regression models that predicts pipeline pressure in minutes are proposed. Random forest, support vector regression (SVR), long-short term memory (LSTM) algorithms are used to build pressure prediction models. A comparison of pressure prediction models' preformances shows that the LSTM model was the best. LSTM model for Asan-si have root mean square error (RMSE) 0.011, mean absolute percentage error (MAPE) 0.494. LSTM model for Cheonan-si have RMSE 0.015, MAPE 0.668.

• Journal of KIISE:Databases
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• v.35 no.4
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• pp.319-332
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• 2008

Data cube, which is multi-dimensional data model, have been successfully applied in many cases of multi-dimensional data analysis, and is still being researched to be applied in data stream analysis. Data stream is being generated in real-time, incessant, immense, and volatile manner. The distribution characteristics of data arc changing rapidly due to those characteristics, so the primary rule of handling data stream is to check once and dispose it. For those characteristics, users are more interested in high support attribute values observed rather than the entire attribute values over data streams. This paper propose dynamic data cube for applying data cube to data stream environment. Dynamic data cube specify user's interested area by the support ratio of attribute value, and dynamically manage the attribute values by grouping each other. By doing this it reduce the memory usage and process time. And it can efficiently shows or emphasize user's interested area by increasing the granularity for attributes that have higher support. We perform experiments to verify how efficiently dynamic data cube works in limited memory usage.

• Journal of KIISE:Computing Practices and Letters
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• v.6 no.2
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• pp.113-119
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• 2000

In this paper, we design and implement a query processor of real-time main memory database systems, which reflect the characteristics of main memory database systems and satisfy timing constraints. The proposed query processor manages real-time data that has timing constraint by exploiting meta database. It supports CLI in order to make application programs. It also supports extended CLI and stored CLI. The former can be expressed the Information on real-time transaction. The latter is designed to support frequently processed transaction. The proposed query processor is implemented as query processor of real-time database management systems. We Present performance evaluation results that illustrate ratio of transaction, which satisfy deadline are increased by the query processing ability of system and the efficient management of real-time data.

• Journal of Haehwa Medicine
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• v.12 no.1
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• pp.11-26
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• 2003

Alzheimer's disease (AD) is characterized by amyloid deposition and associated loss of neunons in brain regions involved in learning and memory processes. Several causes of evidence support that the congnitive disturbance is closed associated with the deficit of cerebral acetylcholine neurotransmission, and the effect of carboxyl terminal 105 amino acid fragment (CT105) of the amyloid precursor protein (APP) on the gene expression of proinflammatory cytokines. We tested it on the scopolamine-induced amnesia model of the ICR mouse using the Morris water maze with repeated orally administration of 1st Green-Tea extract (200 mg/kg) and 2nd Green-Tea extract (200 mg/kg). The Green-Tea prevents impairment of learning and memory and neuronal loss in mouse models of cognitive disturbance and it demonstrated selectivity for inhibition of acetylcholinesterase (AChE). Furthermore, the repeated administration of Green-Tea, CT105-induced alzheimer's mouse model showed central cholinergic activity by ameliorates learning and memory impairment, and isolation of CD14 microglia showed significantly decreases intracellular release of the proinflammatory cytokines tumor necrosis factor-${\alpha}$, interleukin-$1{\beta}$ and reactive oxygen species (ROS). Because of its composite profile, oral therapeutic index and a prophylactic, Green-Tea is considered the better therapeutic candidate for the treatment of Alzheimer's disease.

• Smart Structures and Systems
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• v.1 no.2
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• pp.141-158
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• 2005

A new design of distributed crack sensors based on the topological change of transmission line cables is presented for the condition assessment of reinforced concrete (RC) structures during and immediately after an earthquake event. This study is primarily focused on the performance of cable sensors under dynamic loading, particularly a feature that allows for some "memory" of the crack history of an RC member. This feature enables the post-earthquake condition assessment of structural members such as RC columns, in which the earthquake-induced cracks are closed immediately after an earthquake event due to gravity loads, and are visually undetectable. Factors affecting the onset of the feature were investigated experimentally with small-scale RC beams under cyclic loading. Test results indicated that both crack width and the number of loading cycles were instrumental in the onset of the memory feature of cable sensors. Practical issues related to dynamic acquisition with the sensors are discussed. The sensors were proven to be fatigue resistant from shake table tests of RC columns. The sensors continued to show useful performance after the columns can no longer support additional loads.

• International Journal of Computer Science & Network Security
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• v.21 no.3
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• pp.212-218
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• 2021

Low-power and Lossy Networks (LLNs) have become the common network infrastructure for a wide scope of Internet of Things (IoT) applications. For efficient routing in LLNs, IETF provides a standard solution, namely the IPv6 Routing Protocol for LLNs (RPL). It enables effective interconnectivity with IP networks and flexibly can meet the different application requirements of IoT deployments. However, it still suffers from different open issues, particularly in large-scale setups. These include the node unreachability problem which leads to increasing routing losses at RPL sink nodes. It is a result of the event of memory overflow at LLNs devices due to their limited hardware capabilities. Although this can be alleviated by the establishment of multi-sink topologies, RPL still lacks the support for effective load balancing among multiple sinks. In this paper, we address the need for an efficient multi-sink load balancing solution to enhance the performance of PRL in large-scale scenarios and alleviate the node unreachability problem. We propose a new RPL objective function, Multi-Sink Load Balancing Objective Function (MSLBOF), and introduce the Memory Utilization metrics. MSLBOF enables each RPL node to perform optimal sink selection in a way that insure better memory utilization and effective load balancing. Evaluation results demonstrate the efficiency of MSLBOF in decreasing packet loss and enhancing network stability, compared to MRHOF in standard RPL.